Rapid and accurate multiple testing correction and power estimation for millions of correlated markers.

ABSTRACT: With the development of high-throughput sequencing and genotyping technologies, the number of markers collected in genetic association studies is growing rapidly, increasing the importance of methods for correcting for multiple hypothesis testing. The permutation test is widely considered the gold standard for accurate multiple testing correction, but it is often computationally impractical for these large datasets. Recently, several studies proposed efficient alternative approaches to the permutation test based on the multivariate normal distribution (MVN). However, they cannot accurately correct for multiple testing in genome-wide association studies for two reasons. First, these methods require partitioning of the genome into many disjoint blocks and ignore all correlations between mark

SUBMITTER: Han B 

PROVIDER: S-EPMC2663787 | biostudies-literature | 2009 Apr

REPOSITORIES: biostudies-literature